class_docstr: FPN_CARAFE is a more flexible implementation of FPN. It allows more
  choice for upsample methods during the top-down pathway.
  
  It can reproduce the preformance of ICCV 2019 paper
  CARAFE: Content-Aware ReAssembly of FEatures
  Please refer to https://arxiv.org/abs/1905.02188 for more details.
  
  Args:
      in_channels (list[int]): Number of channels for each input feature map.
      out_channels (int): Output channels of feature pyramids.
      num_outs (int): Number of output stages.
      start_level (int): Start level of feature pyramids.
          (Default: 0)
      end_level (int): End level of feature pyramids.
          (Default: -1 indicates the last level).
      norm_cfg (dict): Dictionary to construct and config norm layer.
      activate (str): Type of activation function in ConvModule
          (Default: None indicates w/o activation).
      order (dict): Order of components in ConvModule.
      upsample (str): Type of upsample layer.
      upsample_cfg (dict): Dictionary to construct and config upsample layer.
docstr: Slice ``src`` as ``dst``
  
  Note:
      ``src`` should have the same or larger size than ``dst``.
  
  Args:
      src (torch.Tensor): Tensors to be sliced.
      dst (torch.Tensor): ``src`` will be sliced to have the same
          size as ``dst``.
  
  Returns:
      torch.Tensor: Sliced tensor.
docstr_tok: ["Slice","``","src","``","as","``","dst","``","Note",":","``","src","``","should","have","the","same","or","larger","size","than","``","dst","``",".","Args",":","src","(","torch.Tensor",")",":","Tensors","to","be","sliced",".","dst","(","torch.Tensor",")",":","``","src","``","will","be","sliced","to","have","the","same","size","as","``","dst","``",".","Returns",":","torch.Tensor",":","Sliced","tensor","."]
code: def slice_as(self, src, dst):
      """Slice ``src`` as ``dst``
  
      Note:
          ``src`` should have the same or larger size than ``dst``.
  
      Args:
          src (torch.Tensor): Tensors to be sliced.
          dst (torch.Tensor): ``src`` will be sliced to have the same
              size as ``dst``.
  
      Returns:
          torch.Tensor: Sliced tensor.
      """
      assert (src.size(2) >= dst.size(2)) and (
          src.size(3) >= dst.size(3)
      )
      if src.size(2) == dst.size(2) and src.size(3) == dst.size(3):
          return src
      else:
          return src[:, :, : dst.size(2), : dst.size(3)]
  
file_path: insightface/detection/scrfd/mmdet/models/necks/fpn_carafe.py
imports: import torch.nn
  from mmcv.cnn import ConvModule, build_upsample_layer, xavier_init
  from mmcv.ops.carafe import CARAFEPack
  from ..builder import NECKS
label_desc: Use image node_id 3 for calling the FPN_CARAFE obj's underlying member method code with example usage: obj.slice_as(src, dst) and returns: src, src